The Impact Of Artificial Intelligence Tools Utilization on Student Satisfaction in Case Study-Based Learning

Abstract

The increasing adoption of artificial intelligence (AI) tools in case-based learning (CBL) has increased significantly in higher education settings; however, evidence regarding their impact on student satisfaction remains limited. This invenstigates the association between the use of AI tools and student satisfaction through an integrated framework drawing upon the Technology Acceptance Model (TAM), UTAUT2, and the DeLone and McLean Information Systems Success Model. The study employed a quantitative approach utilizing a structured questionnaire was employed, yielding 250 valid responses, which were subsequently assessed using Partial Least Squares Structural Equation Modeling (PLS-SEM) after removing multivariate outliers based on Mahalanobis distance. The analysis reveals significant effects of Perceived Usefulness, Content Quality, and Performance Expectancy on Behavioral Intention, while Perceived Ease of Use shows no statistically significant effect on this construct. Furthermore, Behavioral Intention significantly influences Student Satisfaction. The results further indicate that students generally tend to prioritize the usefulness, content quality, and contribution to learning performance of AI tools, rather than on their ease of use.